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Rethinking AI Infrastructure: Why the Workstation Is Back

Rethinking AI Infrastructure: Why the Workstation Is Back

Artificial intelligence isn’t just transforming what businesses do—it’s transforming where and how they do it. Across industries, AI is moving closer to the edge. Models are being trained and run not just in cloud environments, but also in offices, labs, studios, and even factory floors.

This shift is creating new pressures on infrastructure—pressures that the cloud alone can’t relieve.

 

Why AI Can’t Always Wait for the Cloud

Large-scale AI models require enormous amounts of processing power, memory, and bandwidth. And while public cloud services offer scale, they also introduce challenges: long provisioning times, rising costs, and performance constraints when latency matters.

Modern AI applications—like fine-tuning models on proprietary datasets, real-time inference, or running simulations—demand local, immediate performance. They need compute resources to be where the data is, not a region away.

Local Workstations Are Stepping In

AI workstations are becoming the go-to option for teams who need data center-level performance without relying exclusively on the data center. Systems like HP’s Z8 Fury and Z8 G5, equipped with up to four NVIDIA RTX™ 6000 Ada GPUs, deliver 192GB of GPU memory and nearly 6 petaflops of AI compute.

With that kind of power, teams can:

  • Train models on sensitive internal data—without exporting it

  • Run multiple GPU-heavy apps at once (simulation, modeling, rendering)

  • Handle real-time decision-making and inferencing at the edge

  • Accelerate exploration, testing, and deployment with local control

Where Local AI Is Already Making a Difference

Architecture, Engineering & Construction (AECO):
AI-enhanced design workflows demand rapid iteration. Local workstations let teams test energy models, structural simulations, and generative layouts in real time.

Manufacturing:
Digital twins, layout optimization, and predictive maintenance all benefit from GPU-powered compute on-site—especially when time and precision are critical.

Creative & Media Production:
AI is now embedded in editing, rendering, and effects pipelines. From Redshift to Unreal Engine, content creators rely on local horsepower to keep projects moving without delays.

Data Science & AI Research:
Fast model development depends on tight feedback loops. Workstations give teams a consistent, responsive environment with preloaded tools like PyTorch, TensorFlow, and RAPIDS via the Z by HP Data Science Stack Manager.

The Hybrid Future: Smarter, Not Bigger

Workstations don’t replace the cloud. But they offer a smarter approach to hybrid infrastructure—one that gives IT teams more control over cost, performance, and security.

  • Run sensitive or performance-intensive workloads locally

  • Shift to cloud or data center when scale demands it

  • Empower remote teams through secure access with HP Anyware

This balance reduces cloud dependency while maximizing agility. And it ensures that AI infrastructure adapts to the task—not the other way around.

Designed for IT, Built for Business

IT leaders need reliable, secure, and manageable infrastructure that doesn’t slow innovation. NVIDIA RTX™-powered Z by HP workstations are ISV-certified, remotely manageable, and built to run 24/7 in professional environments.

With HP Anyware, teams can securely access powerful Z workstations from anywhere—giving hybrid and remote users the same performance they’d expect in the office.

And with HP Wolf Security, your AI and data workflows are protected from BIOS to browser, reducing risk while maintaining compliance.

Final Word: Ready for What AI Demands Next

AI isn’t slowing down, and neither should your infrastructure. The demands of generative tools, data-heavy simulations, and edge inferencing require more than just available compute—they require the right compute, in the right place, at the right time.

HP’s Z workstations, powered by NVIDIA RTX™, offer a proven solution: local performance, enterprise reliability, and scalable flexibility. For IT leaders navigating the next wave of AI adoption, that’s a foundation worth building on. Contact us to learn more.

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Voyager Labs – Leveraging Open Source Data and AI

Leveraging Open Source Data and AI for National Labs Priorities:

Countering Hostile Foreign Intelligence Threats, Insider Threats, and Cyber Threats

National Labs IT Summit (NLIT)
Wednesday, April 10 1:35 PM PDT
Room 618

Voyager Labs will address and demonstrate advanced open source data analytics software and methodologies to optimize identifying and countering threats to National Labs personnel and facilities from various threats. Through the application of advanced analytics and artifical intelligence solutions, emerging and present threats can be detected, identified, avoided, and countered in a heavily automated and continuous manner. The emphasis is on supply chain risk management, hostile foreign intelligence threats, and insider threats, including threats from spyware, malware, and ransomware. This presentation will also cover the positive and immediate impact such software can provide for recruitment and retention of specialized personnel and the management of crises and public relations requirements.

The rapid and constant proliferation of open-source data can be a key part in helping enterprises address critical security threats and business needs. The work of the National Labs is sensitive, classified, and of great interest to many hostile foreign intelligence services which have well-resourced, expert, and determined programs to acquire such US government secrets. The methodologies these foreign governments and organizations persistently employ include the development and attempted recruitment of personnel with access to sensitive information and the insertion of technical espionage means, including in hardware and software. Countering these threats by leveraging open source data search and analysis capabilities – including from global platforms and in foreign languages – is most effective when experts in threat analysis, cyber, supply chain risk management, counter-intelligence, and operational security can use proven, tested, and effective analytic tools making use of machine learning and AI disciplines. The benefits of collecting and analyzing the constantly growing ocean of new data points relevant to any government or business enterprise are also evident with positive and high-impact results for identifying and recruiting highly skilled staff, vetting job applicants and students for security and suitability issues, managing crises, and informing effective public relations strategies.

The federal government and other public sector organizations, as well as Fortune 500 companies are currently, successfully applying these software solutions and methodologies. The notable effectiveness of these solutions has been enhanced by the ease of learning and application of the software, the constant evolution and improvement of AI through close feedback loops with software clients for tailored results based on their priority requirements, and the speed and depth of data collection and analysis. The software is deployed by agencies as an enterprise-level solution running on FedRAMP-certified Clouds or On-Premise, with collected data easily exported via API or Client-specific formats. The analytic and AI solutions can also be applied to agencies’ sensitive proprietary data, if needed, and serve as a valuable component in Zero Trust strategies and implementations.

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